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On robust parameter estimation in brain-computer interfacing

Authors
Samek, WojciechNakajima, ShinichiKawanabe, MotoakiMueller, Klaus-Robert
Issue Date
12월-2017
Publisher
IOP PUBLISHING LTD
Keywords
brain-computer interfacing; parameter estimation; common spatial patterns; robustness
Citation
JOURNAL OF NEURAL ENGINEERING, v.14, no.6
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF NEURAL ENGINEERING
Volume
14
Number
6
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/81388
DOI
10.1088/1741-2552/aa8232
ISSN
1741-2560
Abstract
Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.
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